Machines, also known as plants, continue to evolve into ever more complex systems and subsystems. For example, motor vehicles, copiers, paper making machines and the like continue to be developed with increasing numbers of sensors, increased quality control, improved safety standards, performance enhancements and the like. Such machines typically provide, or seek to provide, increased convenience for a user, improved quality of a product, etc. However, repairing such machines also continues to become more complex and expensive. For example, diagnosis of a problem within such a machine can take extended periods of time if the root cause for the problem is not identified in a relatively short time. In addition, repair manuals and the level of expertise of technicians can be inadequate for complex problem diagnosis and/or to solve problems that have yet to be identified in a new machine. As such, an advisory system that uses artificial intelligence to display probabilistic causes of a problem in a machine in order to aid a technician in troubleshooting the problem would be desirable.